@inproceedings{chakravartula-indurthi-2019-emominer,
    title = "{EMOMINER} at {S}em{E}val-2019 Task 3: A Stacked {B}i{LSTM} Architecture for Contextual Emotion Detection in Text",
    author = "Chakravartula, Nikhil  and
      Indurthi, Vijayasaradhi",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2033/",
    doi = "10.18653/v1/S19-2033",
    pages = "205--209",
    abstract = "This paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context of a text conversation. Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pre-trained on a large collection of Twitter data. In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task."
}